Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images (2006.13276v1)

Abstract: The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While real-time RT-PCR is the most commonly used, these can take up to 8 hours, and require significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.

• The paper presents a novel momentum contrastive learning framework that enhances few-shot COVID-19 diagnosis from chest CT images.
• It pre-trains an encoder with contrastive visual embeddings and uses stochastic augmentation to overcome the challenges of limited labeled data.
• Experimental results demonstrate superior performance compared to models like ResNet-50 and DenseNet-121, offering robust diagnostic accuracy.

Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images

The research paper "Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images" explores an innovative approach for the rapid and automatic diagnosis of COVID-19 using chest CT scans. The primary focus is on addressing the constraints posed by limited labeled datasets, which are a significant hindrance in the training of deep learning models.

Overview and Methodology

To surmount the scarcity of annotated CT images, the paper introduces a deep learning framework leveraging contrastive learning alongside few-shot learning paradigms. The method employs a contrastive learning approach to pre-train an encoder on extensive unlabeled lung datasets, followed by a few-shot classification using prototypical networks. This hybridization aims to enable effective generalization even with minimal training samples.

Key methodological components include:

1. Data Augmentation: Stochastic processes are applied to augment dataset images into various format views, enhancing the training dataset's variability without introducing labeled data augmentation complexities.
2. Contrastive Learning: By employing a contrastive visual embedding, the network maximizes feature representation agreement between different augmented views of the same data point. A momentum contrast mechanism mitigates inconsistencies in encoded keys, leveraging a dynamic dictionary for query-key look-ups.
3. Few-shot Classification: The paper validates its contrastive trained encoder via prototypical networks which, by employing metric learning, map query and support set images into an embedding space where classification is executed by proximity measures.

Experimental Findings

The paper finds the proposed model to deliver promising diagnostic accuracy with a few labeled samples, demonstrating superior performance compared to other baselines, including ResNet-50, DenseNet-121, and variants trained on ImageNet. Experimental results indicated a notable increase in performance with increased shot numbers (number of samples per class), highlighting the robustness of the model in data-scarce scenarios.

Implications and Future Directions

Practically, the implications of this work are profound, providing potential pathways for scaling rapid diagnostic tools amidst pandemics where data collection is constrained. Theoretically, the introduction of momentum contrastive learning in medical imaging sets a precedent for exploration in other domains where labeled data is rare yet imperative, such as rare diseases or personalized medicine.

Future research could expand on this methodology by exploring more complex pre-text tasks within self-supervised paradigms or integrating semi-supervised learning strategies to further leverage available unlabeled data. Improved model architectures might unlock further efficiencies and accuracies, while the proposed framework's application could broaden to other imaging modalities beyond CT scans.

In summary, this paper contributes significantly to the discourse on efficient, scalable solutions for medical image analysis, emphasizing the utility of deep learning techniques in overcoming data limitations while enhancing diagnostic capabilities in critical healthcare scenarios.